Results

- Evolution of dispersal and life history strategies – Tetrahymena ciliates

Growth from low density in presence of nutrients

All strains displayed a similar pattern of logistic growth, first exponential growth then reaching a plateau, where cells became smaller and rounder in a first time due to the rapid cell divisions and returned to their starting size and shape values once again when reaching the plateau. However, strains differed quantitatively. Growth rate and carrying capacity were largely strain-dependent (ΔAIC model with no effect versus model with a strain effect = 222.73), while variation between replicates within strain showed significant variation for some strains only and to a much lesser extent (ΔAIC model with a strain effect versus model with strain and replicate effects = 4.74). At the end of the experiment, where strains were at their carrying capacity, strains also differed in cell density (F9,18 = 10.72, P < 0.0001), size (F9,18= 17.47, P < 0.0001), and shape (F9,18 = 18.95, P < 0.0001).

Strains differed with respect to the co-variation among traits as well. This was shown by a principal component analysis on growth rate, final cell density, final cell size and final cell shape (carrying capacity K was dropped because it correlated tightly with final cell density), followed up by a discriminant analysis on the principal components (PCs). Such discriminant analyses test whether strains can be significantly distinguished from each other on the basis of the variables under study; they do not force artefactual strain separation [94]. All PCA components were significantly implicated in discriminating strains (PC1G: F9,20 = 48.55, P < 0.0001, R2 = 0.96; PC2G: F9,20 = 2.45, P = 0.045, R2 = 0.52; PC3G: F9,20 = 8.40, P < 0.0001, R2 = 0.79) with 80 % of the replicates well classified within their strain. PC1G (explaining 46 % of the variance) represented a factor that was negatively associated with final cell size and positively with final cell shape (Figure 1). This means that at the carrying capacity some strains had small and elongated cells, others rounder and bigger cells. PC2G (explaining 27 % of the variance) represented a contrast between growth rate and final cell density (Figure 1). Thus, some strains grew rapidly in the beginning but reached only a low final cell density (r strategy), whereas others grew slowly in the beginning but reached a high final density (K strategy). PC3G (explaining 20 % of the variance) represented the overall performance of growth (r and K) from low density in the presence of nutrients, contrasting strains performing well (e.g. 7) to strains performing less well (e.g. D3) (Figure 1).

Starvation in a medium devoid of nutrients

At the start of the experiment, cells were still dividing as if they were in a nutrient-rich environment. Although there was some variation between replicates and strains, a peak was observed at 8 h for density and cell elongation. Then density was steadily decreasing and cell shape rounding. Strains, however, differed significantly in survival rate, as estimated by survival as a density sum over time (F9,20 = 55.70, P < 0.0001). They also differed significantly as regarded cell elongation in response to starvation, as measured by mean (F9,20 = 90.96, P < 0.0001) and variance (F9,20 = 6.11, P = 0.0004) of maximal elongation, elongation persistence (F9,20 = 742.96, P < 0.0001) and frequency of disperser morphs (F9,20 = 15.80, P < 0.0001).

Strains differed regarding the impact of starvation on trait associations, too, as was shown by a principal component analysis on cell survival, elongation, and production of disperser morphs, followed up by a discriminant analysis on the principal components (PCs). The first two PCA components explained 81 % of the variance in the data, were highly significantly implicated in discriminating strains (PC1S: F9,20 = 106.73, P < 0.0001; PC2S: F9,20 = 19.67, P < 0.0001) with all replicates well classified within their strain (P = 1). The first principal component, PC1S, (explaining 57 % of the variance) expressed the overall survival performance, cell elongation and production of disperser morphs in starvation conditions, positively associated with all five included variables (Figure 2). PC2S (explaining 24 % of the variance) represented a contrast of elongation reactions: strains with a larger and persisting mean maximal elongation but a limited variance in elongation (everyone elongates relatively similarly) versus strains with a larger variation between cells, some elongating far more than other, up to becoming real disperser morphs (Figure 2).

Dispersal in presence of nutrients

The rates at which T. thermophila cells dispersed from one tube (the start patch) through a connecting tubing to another tube (the target patch) were strongly positively correlated with the degree of cell elongation (Figure 3) and with the initial shape of cells(r = 0.34, n = 55, P = 0.011). A linear model analysis revealed that the differences in dispersal rate were primarily situated between strains (F9,43 = 10.64, P < 0.0001). When controlling for this strain effect, the correlation of dispersal rate with cell elongation was still significant (F1,43 = 21.46, P < 0.0001), but not the correlation with initial shape (F1,43 = 0.15, P = 0.701). Elongation also significantly differed between strains (F9,43 = 3.01, P = 0.007). Our tests hence showed that dispersal rates vary significantly among strains and also supported that elongation of T. thermophila cells is linked with dispersal.

The above associations were well represented by the first two axes of a final principal component analysis (Figure 5) performed on the seven variables summarizing the four experiments (dispersal rate, colonization probability, survival and elongation in presence of nutrients PC1G, contrast between growth rate and final cell density PC2G, growth performance PC3G, survival and elongation under starvation PC1S, elongation strategy PC2S; Figure 4). The first axis of this comprehensive PCA explained on average 39% of the variance (SD: 1.6%; range: 34% to 44%, over 1000 random associations of replicates across experiments) and the second axis 23% of the variance (SD: 1.4%; range: 19% to 28%). These two axes allowed discriminating strains very efficiently, with only on average 16% (SD: 7.7%; minimum, median, maximum: 0%, 16%, 37%, respectively) of the replicates not correctly classified. Some strains (e.g. 7 and D2) were always perfectly discriminated, and misclassifications only concerned a single replicate for the majority of the strains; only B strain was more frequently misclassified for all replicates.